TY - GEN
T1 - Co-Clustering Structural Temporal Data with Applications to Semiconductor Manufacturing
AU - Zhu, Yada
AU - He, Jingrui
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Recent years have witnessed data explosion in semiconductor manufacturing due to advances in instrumentation and storage techniques. In particular, following the same recipe for a certain IC device, multiple tools and chambers can be deployed for the production of this device, during which multiple time series can be collected, such as temperature, impedance, gas flow, electric bias, etc. These time series naturally fit into a two-dimensional array (matrix), i.e., Each element in this array corresponds to a time series for one process variable from one chamber. To leverage the rich structural information in such temporal data, in this paper, we propose a novel framework named C-Struts to simultaneously cluster on the two dimensions of this array. In this framework, we interpret the structural information as a set of constraints on the cluster membership, introduce an auxiliary probability distribution accordingly, and design an iterative algorithm to assign each time series to a certain cluster on each dimension. To the best of our knowledge, we are the first to address this problem. Extensive experiments on benchmark and manufacturing data sets demonstrate the effectiveness of the proposed method.
AB - Recent years have witnessed data explosion in semiconductor manufacturing due to advances in instrumentation and storage techniques. In particular, following the same recipe for a certain IC device, multiple tools and chambers can be deployed for the production of this device, during which multiple time series can be collected, such as temperature, impedance, gas flow, electric bias, etc. These time series naturally fit into a two-dimensional array (matrix), i.e., Each element in this array corresponds to a time series for one process variable from one chamber. To leverage the rich structural information in such temporal data, in this paper, we propose a novel framework named C-Struts to simultaneously cluster on the two dimensions of this array. In this framework, we interpret the structural information as a set of constraints on the cluster membership, introduce an auxiliary probability distribution accordingly, and design an iterative algorithm to assign each time series to a certain cluster on each dimension. To the best of our knowledge, we are the first to address this problem. Extensive experiments on benchmark and manufacturing data sets demonstrate the effectiveness of the proposed method.
KW - co-clustering
KW - structural
KW - temporal
UR - http://www.scopus.com/inward/record.url?scp=84936971698&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84936971698&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2014.17
DO - 10.1109/ICDM.2014.17
M3 - Conference contribution
AN - SCOPUS:84936971698
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 1121
EP - 1126
BT - Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014
A2 - Kumar, Ravi
A2 - Toivonen, Hannu
A2 - Pei, Jian
A2 - Zhexue Huang, Joshua
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE International Conference on Data Mining, ICDM 2014
Y2 - 14 December 2014 through 17 December 2014
ER -